An image super-resolution reconstruction method based on deep learning iterative up-down sampling
A technology of super-resolution reconstruction and deep learning, which is applied in the field of image super-resolution reconstruction based on deep learning iterative up-down sampling, can solve problems such as difficult application of magnification and inability to express high-level features, so as to improve sensitivity, The effect of increasing the receptive field and making the reconstruction process easier
Active Publication Date: 2021-04-02
CHENGDU UNIV OF INFORMATION TECH
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Although compared with the previous traditional method, the traditional image super-resolution algorithm based on shallow learning has been greatly improved, such as sharper, but because the features extracted by this method are artificially designed, it cannot express high-level features, so super-resolution tasks with large magnifications are still difficult to apply to this method
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[0037] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in combination with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are exemplary only, and are not intended to limit the scope of the present invention. Also, in the following description, descriptions of well-known structures and techniques are omitted to avoid unnecessarily obscuring the concept of the present invention.
[0038] A detailed description will be given below in conjunction with the accompanying drawings.
[0039] Based on the deep learning theory, the present invention constructs an end-to-end network, which is based on iterative up-down sampling, adding a hole convolution network to extract the context information in the image, and combining all the convolution layers in the up-down sampling Use hole convolution, that is, the ...
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The invention relates to an image super-resolution reconstruction method based on deep learning iterative up-down sampling. The method includes: preparing an original high-resolution image, performing bicubic interpolation on the original high-resolution image to obtain a low-resolution image, and converting the low-resolution The image is input into the built neural network, and the low-resolution image features are extracted first, and then the high-resolution image features are extracted layer by layer through the up-down sampling module of multiple back-projection layers, so as to be able to mine more low-resolution images The interdependence between images and high-resolution images, the convolution in the neural network uses hollow convolution to increase the receptive field, improve the sensitivity of the network to feature information, and introduce dense connections to reduce the information passing through the middle layer. Lost, it can make better use of low-level feature information and improve the performance of image reconstruction.
Description
technical field [0001] The invention relates to the field of image processing, in particular to an image super-resolution reconstruction method based on deep learning iterative up-down sampling. Background technique [0002] The application field of image super-resolution reconstruction is extremely broad, and it has important application prospects in military, medical, public security, and computer vision. For example, high-resolution medical images are very helpful for doctors to make correct diagnoses; using high-resolution satellite images, it is easy to distinguish similar objects from similar objects; if high-resolution images can be provided, computer vision The performance of pattern recognition will be greatly improved. The most direct way to improve image resolution is to improve the optical hardware in the acquisition system, but this method is limited by the constraints of difficult to greatly improve the manufacturing process and high manufacturing costs. Ther...
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IPC IPC(8): G06T3/40G06N3/04G06N3/08
CPCG06T3/4076G06N3/08G06N3/045
Inventor 胡靖李欣妍吴锡
Owner CHENGDU UNIV OF INFORMATION TECH